ArticlePhysics in medicine and biology2015
Computer-aided detection system for clustered microcalcifications in digital breast tomosynthesis using joint information from volumetric and planar projection images.
Article in Physics in medicine and biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 29 citations in OpenAlex.
- Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs.Journal of imaging · 2022Article
- Deep Convolutional Neural Network With Adversarial Training for Denoising Digital Breast Tomosynthesis Images.IEEE transactions on medical imaging · 2021Article
- CAD and AI for breast cancer-recent development and challenges.The British journal of radiology · 2020Review
- Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets.IEEE transactions on medical imaging · 2019Article
- Improving image quality for digital breast tomosynthesis: an automated detection and diffusion-based method for metal artifact reduction.Physics in medicine and biology · 2017Article
- Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning.Scientific reports · 2017Article
- Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network-A Pilot Study.Tomography (Ann Arbor, Mich.) · 2016Article
- Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.Medical physics · 2016Article
- Analysis of computer-aided detection techniques and signal characteristics for clustered microcalcifications on digital mammography and digital breast tomosynthesis.Physics in medicine and biology · 2016Article
- Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets.Medical physics · 2016Article
- Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis.BioMed research international · 2016Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
We propose a novel approach for the detection of microcalcification clusters (MCs) using joint information from digital breast tomosynthesis (DBT) volume and planar projection (PPJ) image. A data set of 307 DBT views was collected with IRB approval using a prototype DBT system. The system acquires 21 projection views (PVs) from a wide tomographic angle of 60° (60°-21PV) at about twice the dose of a digital mammography (DM) system, which allows us the flexibility of simulating other DBT acquisition geometries using a subset of the PVs. In this study, we simulated a 30° DBT geometry using the central 11 PVs (30°-11PV). The narrower tomographic angle is closer to DBT geometries commercially available or under development and the dose is matched approximately to that of a DM. We developed a new joint-CAD system for detection of clustered microcalcifications. The DBT volume was reconstructed with a multiscale bilateral filtering regularized method and a PPJ image was generated from the reconstructed volume. Task-specific detection strategies were designed to combine information from the DBT volume and the PPJ image. The data set was divided into a training set (127 views with MCs) and an independent test set (104 views with MCs and 76 views without MCs). The joint-CAD system outperformed the individual CAD systems for DBT volume or PPJ image alone; the differences in the test performances were statistically significant (p < 0.05) using JAFROC analysis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.